For decades, the product development process (PDP) was a bastion of predictability. In the traditional era of manufacturing, functions were primarily mechanical and required a manageable set of components: typically one electronic control unit (ECU), a few sensors, and an actuator. Engineering teams operated within the linear progression of the V-model, where specification, design, and testing followed a structured path. Complexity was linear, and experienced engineers could rely on their intuition, honed over years of hardware-centric cycles, to anticipate how a change in one component might affect the rest of the system.
But that era has ended. The rise of software-defined products (SDVs), such as modern vehicles, aircraft, and defense systems, has fundamentally transformed how value is created and, more importantly, when and where failures surface across the product development lifecycle. As functions shifted from hardware to software, the sheer volume of interdependencies exploded beyond the capacity of any human mind to track.
Engineering intuition is no longer scaling. We are no longer building machines; we are building hyper-connected, software-driven ecosystems that have broken the traditional engineering mental model.
The move toward SDVs has introduced a level of combinatorial complexity that defies traditional collaboration. In the past, a function was realized through roughly three realization components with a large hardware share. Today, the same function is realized through 12 or more components, with a massive software share, including multiple ECUs and complex software modules that interact across domains.
This is not just a marginal increase in complexity; it is an exponential one driven by Reed’s Complexity Law. Reed’s Law, originally formulated for group-forming networks, helps illustrate this dynamic: as more modules or stakeholders are added, the space of potential interactions expands dramatically, far faster than linear growth.
The past: A function managed by 3 people created roughly 4 communication paths.
The present: A function involving 13 people across different disciplines creates 8,178 potential communication paths.
When a modern vehicle integrates more than 20,000 parts, six kilometers of wiring, and over 500 million lines of code, the interactions become unmanageable. Engineers can no longer mentally track these thousands of cross-domain interactions, leading to a reality where dependencies grow faster than organizations can manage them.
The breakdown of intuition is reflected in a widening productivity gap that threatens the foundation of European industrial strength. While automotive software design complexity is projected to grow by 42% per year through 2027, development productivity is only expected to grow by 6% per year.
Traditional tools and processes, designed for a hardware-first world, are struggling to keep up with this complexity. The result is that European automotive programs now take twice as long as those in China, 50 to 60 months compared to the 24 to 36 months achieved by competitors using software-tailored organizations.
This is not a cultural problem of not working hard enough. It is a structural problem; intuition has been replaced by information overload.
What does blind engineering look like in practice? It manifests as a fragmented data ecosystem where critical product information is scattered across siloed PLM, ALM, and ERP systems. Because these tools cannot horizontally connect different domains, engineers spend 75% of their working time searching for data and reconciling conflicting information rather than innovating. (Engineering Intelligence Index by SPREAD, 2025)
In this environment, late surprises are inevitable, not accidental. Because engineers cannot predict how changes in one software module will affect other hardware components, unexpected integration errors surface too late in the process.
Late-stage integration: Unclear architecture interdependencies lead to error discovery only during final testing.
Firefighting: Companies are forced to create expensive task forces to deal with emergencies that should have been prevented years earlier.
The SOP tax: Every day that a Start of Production (SOP) is postponed costs an OEM more than €1 million in lost revenue, while total development costs can spiral to €2–4 billion per model.
These surprises are structural. They are the direct result of trying to manage an exponential, software-defined world with linear, document-driven infrastructure.
To master SDV complexity, the industry must move beyond the firefighting of late errors and embrace a true engineering shift-left. However, shifting left must mean more than just moving a process earlier in the timeline; it must mean proactive understanding.
Current solutions are often vertical and work in silos, managing only structured data while ignoring the tribal knowledge trapped in thousands of unstructured documents like PDFs, Excel sheets, and emails. Regaining control requires a unified intelligence layer that contextualizes all product data into a coherent view.
This is the role of Engineering Intelligence. By using a Knowledge Graph to create a functional Product Twin, organizations can finally see the logical dependencies between hardware and software components. This allows teams to:
Visualize system-wide signal flow: Trace how a function is realized across ECUs and gateways before a single physical prototype is built.
Conduct automated impact analysis: Understand exactly how a change in one requirement propagates through the architecture to affect other variants.
Detect errors early: Match specified vs. actual behavior during development, cutting fault-diagnostic time by up to 80%.
If this sounds familiar, you are not alone.
The structural breakdown of engineering intuition is the defining challenge of the SDV era. The complexity of modern products has simply outpaced the tools we use to build them. But this complexity does not have to be a burden; it can be a competitive advantage if you have the visibility to manage it.
Mastering the transition to software-defined products is the only way to safeguard industrial sovereignty and ensure on-time development.